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Introduction
Have you ever felt surrounded by data, but with the feeling that it is lacking clarity? If so, you are not alone.
According to Flexera 's State of the Cloud Report 2025 study , more than 90% of companies are already operating with a multi-cloud , ie their data circulate between different public, private and local systems. The scale of this distribution grows year by year, but the ability to integrate and take advantage of this data does not always follow the same pace.
What was once just a matter of infrastructure became an operational bottleneck , with duplicate data, incompatible formats, manual flows. In practice, what we see are teams spending too much energy just to ensure that the information comes complete, right and at the right time. And when it doesn't happen, what is lost is not just a time: it's competitiveness .
This is why scale data integration has become a key challenge for those who lead IT and innovation. Resolving this challenge requires more than connectors: it requires applied intelligence. Thus Pipelines Low-Code , orchestration between clouds and the use of artificial intelligence (AI) to enrich, standardize and validate data in real time are the new starting point .
In this article, we show how to transform this complex integration into a fluid, continuous and scalable process , and how Skyone Studio already does it today, efficiently and control from the first data flow.
Good reading!
The puzzle of modern data
Speaking of “data volume” has already become part of the corporate daily life. But the real challenge today is not how much it collects, but where this data is, in which state they come, and how they can be used with confidence . Most companies have realized that their data is not just growing, but spreading. And when what should be a strategic asset behaves as disconnected pieces , the puzzle begins to weigh.
Why is the data everywhere?
It all starts with the search for agility . To accompany the pace of the market, new tools, APIs and cloud services have been incorporated at record speed . At the same time, many legacy systems followed active, feeding critical operations that could not stop.
The result is an increasingly distributed ecosystem : data that are born in an ERP, pass the service platforms, move through mobile applications, and are stored in different environments such as AWS , Azure , Google Cloud and even local seats. Thus, it is no exaggeration to say that today the data live in constant transit .
This movement even expanded possibilities. But it has also created a side effect : the information is everywhere, but it is rarely complete in the same place.
What makes this integration so complex?
This complexity is not just from technology. It is born from the combination of sources diversity, incompatible formats, specific integrations and processes that evolved without central coordination .
In practice, teams spend hours trying to understand where data is, how to turn it, and how they can trust them. Often this effort focuses on operational tasks such as manual adjustments, duplicate checks and endless exchanges between areas. And when all this happens isolated, the potential of the data is lost on the way .
Therefore, the real challenge is to create cohesion where there is dispersion today , without compromising the speed, the autonomy of the teams or the increasing complexity of the multi-cloud .
This is the key turn that we will discuss below: even in such diverse contexts, it is possible to integrate data with fluidity, intelligence and scale?
Multi-cloud and would: allies or villains?
The idea of distributing loads between different cloud providers while applying artificial intelligence to data to generate value sounds like the natural evolution of corporate technology. But in practice, this combination does not always deliver the expected results. Between promise and reality, there is a critical point: the way these elements connect.
Multi-cloud and Ia are not magical solutions, but powerful tools that can accelerate data use on scale, depending on how they are applied . Let's understand better what is at stake?
Multi-cloud : Freedom with complexity
The choice of multiple clouds is often a strategic decision . It offers autonomy compliance requirements and ensures resilience in the face of failures.
But this gain of flexibility charges a price: different architectures, rules, security standards and data formats living in the same environment. Without a clear layer of orchestration, which was freedom becomes overload. And who feels this in everyday life is the teams that need to integrate information from various sources to run business processes with fluidity.
When the connections are fragile or the data is not complete, agility is lost and the dependence on manual corrections is increased . No wonder so many teams today seek a more visual, continuous and intelligent way to control this flow-which leads us to the role of AI in this puzzle.
Applied to data integration
If I was once seen only as an advanced analysis feature, today it begins to assume a quieter but decisive role within the journey of data.
We are talking about models that act directly on integration flows , learning from historical standards, filling gaps, identifying anomalies and suggesting real -time adjustments. All this, without stopping the pace of the business. It is this shipped intelligence that allows you to automate what was once done "on the arm." And, more than that, create confidence in the data that circulate between systems.
In practice, the well -applied AI reduces rework, raises the quality of information, and prepares the base for actual data -oriented decisions to happen more safely.
This layer of intelligence is already beginning to change the game in many companies. But for it to actually work, you need to face some obstacles that follow present, and make data integration slower, laborious and fragile than you should. It is about these obstacles that we talk next.
The true obstacles to data integration
When speaking of data integration, it is common to imagine that the challenge is only in choosing the right technology. But what blocks data fluidity goes beyond connectors or pipelines . Generally, the blockade is the accumulation of fragile operational practices, decentralized decisions and flows that have grown faster than the ability to structure, standardize and govern.
This distance between what is expected of the data and what they really deliver in practice is visible: misaligned reports, recurring rework, processes that are locking for minimal inconsistencies. And more than a technical problem, it affects the business response time.
Not by chance, the theme “Scale Integration” has been gaining space on IT tables, data and innovation. Below, we map the most common and most costly obstacles in this process.
Lack of quality and consistency
Data quality should be a starting point, but often becomes the main bottleneck. When the data arrive misaligned (either by divergence of nomenclatures, absent fields or incompatible values), the integration becomes slow, laborious and vulnerable .
According to accuisly planning insights 2025 report , 64% of companies still face this problem as a priority, and 67% admit that they do not fully trust the data they use to make decisions . This directly impacts the speed with which new projects can be implemented, and the reliability of the analyzes that guide the operation.
That is, without a clear strategy of standardization and enrichment, teams end up trapped in power cycles that drain energy and prevent evolution to more strategic initiatives.
Governance and compliance under pressure
With data circulating between local systems, multiple clouds and outsourced tools, guaranteeing governance has become a critical mission. It is not just about tracking access or creating permissions, but of understanding the entire information life cycle and having quick answers to questions such as: “Where did this data come from?”, “Who changed?” or "Are we in accordance with LGPD?"
According to Gartner , 75% of governance initiatives fail, precisely due to lack of structure or continuity . And accuisly reinforces this warning in another study : more than half of the companies analyzed still considers governance an obstacle relevant to data integrity .
This scenario compromises not only security but scalability . Without clear governance, dependence on manual processes grows, increases the risk of non-compliance and, especially, visibility is lost-which affects both IT and other business areas.
Disconnected and manual data flows
While many companies advance in modernization initiatives, most data flows still depends on makeshift solutions . Temporary spreadsheets become permanent. Integration
scripts And critical processes require manual checks to avoid predictable failures. Monte Carlo 's State of Data Quality 2023 report shows the cost of this: more than half of companies reported that data quality failures impact up to 25% of their revenue . And the average time to detect these problems increased from 4 to 15 hours in just one year.
This reveals a little resilient operation. When flows are fragile, the error is silent, but the impact is high . And as data becomes more critical to the business, this fragility is no longer only operational: it becomes strategic.
With this data, it is clear that what blocks scale integration is not just the amount of systems. What blocks is the lack of fluidity, standardization and governance behind the scenes. Therefore, in the next block, we will explore how to solve this scenario with more simplicity, intelligence and scale.
Paths to simplify data integration
Getting stuck in manual flows, inconsistencies and rework is not an inevitable destination. With the maturity of tools and data architectures , there are already viable alternatives to integrate more fluidity, even in complex environments.
The key is to stop seeing integration as a punctual effort and begin to treat it as a continuous process , with built-in intelligence from the beginning. Below, we highlight three fronts that have been changing the way companies orchestra their data with more autonomy, scale and reliability.
Pipelines Low-Code : Integration without friction
Pipelines Low-Code are data flows created with minimal coding . Instead of writing scripts , teams draw integrations visually , connecting systems with a few clicks.
This approach reduces development time, diminishes dependence on experts and facilitates adjustments along the way. Thus, IT and data teams gain more autonomy , while the operation becomes more agile and safe.
In multi-cloud , this simplicity makes even more difference. Integration is no longer a technical bottleneck and becomes a continuous capacity, with traceability, easy maintenance and more speed to deliver value.
Modern Architectures: Lakehouse , Mesh and Ipaas
To deal with scale data, you need more than connecting systems. It is necessary to organize the base on which everything happens . And here, three architectures have been standing out:
- Lakehouse : It is a hybrid structure that combines the volume of data lakes with the performance of the data warehouses . It allows you to store large amounts of gross data, but with sufficient structure for fast queries and deep analysis;
- Data Mesh : It is a decentralized approach to data management. Each area of the company becomes responsible for the data it produces, following common standards. This increases team autonomy without compromising consistency;
- IPAAS ( Integration Platform as Service ) : It is a platform that connects different systems through ready -made connectors. It facilitates integration between clouds, banks, ERPs and other services, with native security and scalability.
These architectures are not excluded. On the contrary: When combined, they help organize, distribute and connect data and much more efficiently.
Embedded: From enrichment to intelligent cataloging
Incorporating artificial intelligence into data flows means bringing more autonomy and quality from the base. Embedded AI operates directly on integrations: detects errors, fills gaps, suggests standards, and standardizes real -time formats.
It also allows you to enrich data with external information or with internal history. This increases the context and reliability of analysis without requiring manual work.
Another benefit is intelligent cataloging . With AI, the data is classified, organized and related automatically, which facilitates search, audits and decisions. All this without anyone having to map everything in hand.
These capabilities transform the way data circulates. More than automating, AI helps operate with continuous intelligence and confidence from the beginning.
These three approaches , visual integration, flexible architectures and applied, have a common point: they simplify what was once complex . More than technical solutions, they allow data to circulate with fluidity, structure and intelligence.
But for this to work in everyday life, you need more than good tools. It takes a platform that combines all this with real autonomy, governance and scalability. Let's see how this happens in practice?
How Skyone Studio turns this theory into practice
Everything we have seen so far, from the complexity of flows to shipped intelligence, shows that integrating data efficiently is not only possible: it is essential. And that's exactly what we seek to make real with Skyone Studio .
We created a platform designed to simplify data integration and orchestration into multi-cloud . We use a visual logic, with low-code pipelines , which allows teams to assemble and adjust flows quickly without depending on heavy programming .
We connect natively to different AWS, Azure and Google Cloud environments to local banks and legacy systems. Thus, we guarantee that the data circulate with traceability, security and governance from the origin.
In the intelligence layer, Lakehouse trained AI , using the company's own historical data as a base. This allows us to enrich, standardize and validate information in real time. We have also identified anomalies, filled gaps automatically and optimize the paths for which data traffic.
Our goal is to transform data integration into a fluid, continuous and scalable process . A process that adapts to business needs and follows its growth with confidence and control.
If you want to understand how this can work in the context of your business, we are ready to talk! Talk to one of our experts today and find out what Studio Skyone can simplify, integrate and transform into your business.
Conclusion
Each company carries its own “tangle of data” , with old systems, new tools, forgotten spreadsheets, integrations that no one knows how they work. What we have seen throughout this article is that, behind this complexity, there is an opportunity : to transform the way we deal with the data, with less friction and more intelligence.
This transformation does not need to start over from scratch, but to look at what already exists with another logic. A logic that prioritizes fluidity adapts to the diversity multi-cloud environments and automates what was once done on the basis of improvisation.
This is what we look for with Skyone Studio : Reduce the invisible layers that lock data flows and return clarity to those who need to make decisions. By combining low-code pipelines , cloud connection and was applied from the base, we help turn chaos into continuity, and confidence data .
If you liked this content and want to keep exploring new possibilities for your business, our Skyone Blog is full of possible ideas, provocations and paths. Check out other published content and continue with us on this journey of technological knowledge!
FAQ: Frequently asked how to integrate your data with AI and Multi-Cloud
Data integration in multi-cloud with the support of Artificial Intelligence (IA) still raises many doubts, especially when the goal is to gain scale, control and agility at the same time.
Below are clear and practical answers to some of the most common questions of those who are facing or planning this kind of challenge.
How is AI applied to data integration?
Artificial Intelligence (IA) acts behind the scenes of data flows, automating tasks that previously required a lot of manual effort.
It can detect errors, suggest corrections, fill in gaps based on previous standards, enrich information with historical data, and even identify real -time anomalies. With this, the data gains more quality, consistency and reliability, all with less human intervention.
What makes multi-cloud so challenging?
Managing data in multiple clouds means dealing with different rules, formats, structures and safety requirements. This variety increases the complexity of integration and requires more care with governance and orchestration. Without a light control layer and proper tools, flows become fragile, and the effort to keep the operation grow exponentially.
What are Lakehouse , Mesh and Ipaas and how to choose?
Are complementary approaches to dealing with data complexity:
- Lakehouse : Combines the best of data lakes and data warehouses , allowing to store large volumes with performance for analysis;
- Data Mesh : Distributes responsibility for data between teams, with common rules, which favors autonomy and scalability;
- IPAAS : Connects various systems quickly and governance, ideal for companies that need ready and traceable integrations.
The ideal choice depends on the size of the company, the diversity of data sources and the degree of digital maturity.
